papers

Publications (10)

econ.EM2025

Scenario Synthesis and Macroeconomic Risk

Tobias Adrian, Domenico Giannone, Matteo Luciani +1

We introduce methodology to bridge scenario analysis and model-based risk forecasting, leveraging their respective strengths in policy settings. Our Bayesian framework addresses th…

math.ST2024

Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm

Matteo Barigozzi, Matteo Luciani

We study estimation of large Dynamic Factor models implemented through the Expectation Maximization (EM) algorithm, jointly with the Kalman smoother. We prove that as both the cros…

econ.EM2019

Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models

Matteo Barigozzi, Matteo Luciani

This paper considers estimation of large dynamic factor models with common and idiosyncratic trends by means of the Expectation Maximization algorithm, implemented jointly with the…

stat.ME2017

Common factors, trends, and cycles in large datasets

Matteo Barigozzi, Matteo Luciani

This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood e…

econ.EM2026

Risks and Uncertainty in Monetary Policy

Tobias Adrian, Domenico Giannone, Matteo Luciani +1

Central banks monitor macroeconomic risk through two traditions: scenario analysis, regularly used since the mid-1990s, and distributional forecasting, practiced since the late 196…

econ.EM2025

Measuring the Euro Area Output Gap

Matteo Barigozzi, Claudio Lissona, Matteo Luciani

We measure the Euro Area (EA) output gap and potential output using a non-stationary dynamic factor model estimated on a large dataset of macroeconomic and financial variables. Our…

stat.ME2020

Large-Dimensional Dynamic Factor Models: Estimation of Impulse-Response Functions with Cointegrated Factors

Matteo Barigozzi, Marco Lippi, Matteo Luciani

We study a large-dimensional Dynamic Factor Model where: (i)~the vector of factors is and driven by a number of shocks that is smaller than the dimension of $\…

stat.AP2026

Predictive Synthesis under Sporadic Participation: Evidence from Inflation Density Surveys

Matthew C. Johnson, Matteo Luciani, Minzhengxiong Zhang +1

Central banks rely on density forecasts from professional surveys to assess inflation risks and communicate uncertainty. A central challenge in using these surveys is irregular par…

stat.ME2026

Predictive Concordance for Parameter Optimisation and Mixture Synthesis

Tobias Adrian, Domenico Giannone, Matteo Luciani +1

We discuss probabilistic measures of concordance between two probability distributions based on the expected misclassification rate (EMR). The focus is on comparing a given referen…

math.ST2017

Dynamic Factor Models, Cointegration, and Error Correction Mechanisms

Matteo Barigozzi, Marco Lippi, Matteo Luciani

The paper studies Non-Stationary Dynamic Factor Models such that the factors are and singular, i.e. has dimension and is driven by a -dime…